Contextual Intelligent Decisions: Expert Moderation of Machine Outputs for Fair Assessment of Commercial Driving

• URL: http://arxiv.org/abs/2202.09816v1
• Date: Sun, 20 Feb 2022 13:48:41 GMT
• Title: Contextual Intelligent Decisions: Expert Moderation of Machine Outputs for Fair Assessment of Commercial Driving
• Authors: Jimiama Mafeni Mase, Direnc Pekaslan, Utkarsh Agrawal, Mohammad Mesgarpour, Peter Chapman, Mercedes Torres Torres, Grazziela P. Figueredo
• Abstract summary: We introduce a methodology towards a fairer automatic road safety assessment of drivers' behaviours. The contextual moderation embedded within the intelligent decision-making process is underpinned by expert input. We develop an interval-valued response-format questionnaire to capture the uncertainty of the influence of factors and variance amongst experts' views.
• Score: 2.7323386266136125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Commercial driving is a complex multifaceted task influenced by personal traits and external contextual factors, such as weather, traffic, road conditions, etc. Previous intelligent commercial driver-assessment systems do not consider these factors when analysing the impact of driving behaviours on road safety, potentially producing biased, inaccurate, and unfair assessments. In this paper, we introduce a methodology (Expert-centered Driver Assessment) towards a fairer automatic road safety assessment of drivers' behaviours, taking into consideration behaviours as a response to contextual factors. The contextual moderation embedded within the intelligent decision-making process is underpinned by expert input, comprising of a range of associated stakeholders in the industry. Guided by the literature and expert input, we identify critical factors affecting driving and develop an interval-valued response-format questionnaire to capture the uncertainty of the influence of factors and variance amongst experts' views. Questionnaire data are modelled and analysed using fuzzy sets, as they provide a suitable computational approach to be incorporated into decision-making systems with uncertainty. The methodology has allowed us to identify the factors that need to be considered when moderating driver sensor data, and to effectively capture experts' opinions about the effects of the factors. An example of our methodology using Heavy Goods Vehicles professionals input is provided to demonstrate how the expert-centred moderation can be embedded in intelligent driver assessment systems.

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